<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.7.0.2">
    <description>
        mass spectrometry imaging preprocessing
    </description>
    <requirements>
        <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>
        <requirement type="package" version="2.2.1">r-gridextra</requirement>
        <requirement type="package" version="0.20-35">r-lattice</requirement>
    </requirements>
    <command detect_errors="exit_code">
    <![CDATA[

        #if $infile.ext == 'imzml'
            cp '${infile.extra_files_path}/imzml' infile.imzML &&
            cp '${infile.extra_files_path}/ibd' infile.ibd &&
        #elif $infile.ext == 'analyze75'
            cp '${infile.extra_files_path}/hdr' infile.hdr &&
            cp '${infile.extra_files_path}/img' infile.img &&
            cp '${infile.extra_files_path}/t2m' infile.t2m &&
        #else
            ln -s '$infile' infile.RData &&
        #end if
        cat '${cardinal_preprocessing}' &&
        Rscript '${cardinal_preprocessing}'

    ]]>
    </command>
    <configfiles>
        <configfile name="cardinal_preprocessing"><![CDATA[
library(Cardinal)
library(gridExtra)
library(lattice)

#if $infile.ext == 'imzml'
    msidata <- readMSIData('infile.imzML')
#elif $infile.ext == 'analyze75'
    msidata <- readMSIData('infile.hdr')
#else
    load('infile.RData')
#end if


##################################### Preparations for QC report #######################################################################################

#if $outputs.outputs_select == "quality_control":

    ### values for QC table:
    maxfeatures = length(features(msidata))
    medianpeaks = median(colSums(spectra(msidata)[]>0))
    medint = round(median(spectra(msidata)[]), digits=2)
    TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
    QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs))
    vectorofactions = "rawdata"


        ### Read tabular file with calibrant masses: 
        calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)

            ### calculate how many input calibrant masses are valid: 
            inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column]
            number_calibrants_in = length(calibrant_list[,$outputs.calibrants_column])
            number_calibrants_valid = length(inputcalibrants)




    ### Quality control report


    pdf("Preprocessing.pdf", fonts = "Times", pointsize = 12)
    plot(0,type='n',axes=FALSE,ann=FALSE)
    title(main=paste("Quality control during preprocessing \n", "Filename:", "$infile.display_name"))
    title(main=paste0("\n\n\n\n Number valid masses in ", "$outputs.calibrant_file.display_name",": ", number_calibrants_valid, "/", number_calibrants_in))



    for (calibrant in inputcalibrants)
    {

        currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                       par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                       scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="raw")
      
        assign(paste("rawdata",calibrant, sep="_"), currentimage)
    }


        current_plot_raw <- vector(length(inputcalibrants), mode='list')


#end if

################################################### Preprocessing steps #######################################################################################
#for $method in $methods:
    #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization':
        print('Normalization')
        ##normalization

        msidata <- normalize(msidata, method="tic")

        #if $outputs.outputs_select == "quality_control":
            ### values for QC table:
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))
            medint = round(median(spectra(msidata)[]), digits=2)
            TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
            normalized = c(maxfeatures, medianpeaks, medint, TICs)
            QC_numbers= cbind(QC_numbers, normalized)
            ### preparation for QC plots
            vectorofactions = append(vectorofactions, "normalized")

            for (calibrant in inputcalibrants)
            {
                currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                               par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="normalized")
              
               assign(paste("normalized",calibrant, sep="_"), currentimage)
            }


        #end if

    #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction':
        print('Baseline_reduction')
        ##baseline reduction
        msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline)

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))
            medint = round(median(spectra(msidata)[]), digits=2)
            TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
            baseline= c(maxfeatures, medianpeaks, medint, TICs)
            QC_numbers= cbind(QC_numbers, baseline)
            ### preparation for QC plots
            vectorofactions = append(vectorofactions, "baseline_rem")

            for (calibrant in inputcalibrants)
            {
                currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                               par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="baseline removed")
              
               assign(paste("baseline_rem",calibrant, sep="_"), currentimage)
            }
        #end if

    #elif str( $method.methods_conditional.preprocessing_method ) == 'Smoothing':
        print('Smoothing')
        ## Smoothing

        #if str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'gaussian':
            print('gaussian smoothing')
            msidata <- smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, sd = $method.methods_conditional.methods_for_smoothing.sd_gaussian)
        #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay':
            print('sgolay smoothing')
            msidata <- smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters)
        #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma':
            print('sgolay smoothing')
            msidata <- smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter)
        #end if

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))
            medint = round(median(spectra(msidata)[]), digits=2)
            TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
            smoothed= c(maxfeatures, medianpeaks, medint, TICs)
            QC_numbers= cbind(QC_numbers, smoothed)
            ### preparation for QC plots
            vectorofactions = append(vectorofactions, "smoothed")

            for (calibrant in inputcalibrants)
            {
                currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                               par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="smoothed")
              
               assign(paste("smoothed",calibrant, sep="_"), currentimage)
            }
        #end if

    #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking':
        print('Peak_picking')
        ## Peakpicking

        #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive':
            print('adaptive peakpicking')
            msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method, spar=$method.methods_conditional.methods_for_picking.spar_picking)

        #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'limpic':
            print('limpic peakpicking')
            msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method, thresh=$method.methods_conditional.methods_for_picking.tresh_picking)

        #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'simple':
            print('simple peakpicking')
            msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method)
        #end if

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))
            medint = round(median(spectra(msidata)[]), digits=2)
            TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
            picked= c(maxfeatures, medianpeaks, medint, TICs)
            QC_numbers= cbind(QC_numbers, picked)
            ### preparation for QC plots
            vectorofactions = append(vectorofactions, "picked")

            for (calibrant in inputcalibrants)
            {
                currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                               par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="picked")
              
               assign(paste("picked",calibrant, sep="_"), currentimage)
            }
        #end if

    #elif str( $method.methods_conditional.preprocessing_method ) == 'Peak_alignment':
        print('Peak_alignment')
        ## Peakalignment

        #if str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_noref':
            align_peak_reference = msidata

        #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_table':
            align_reference_table = read.delim("$method.methods_conditional.align_ref_type.align_peaks_table", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)

            align_reference_column = align_reference_table[,$method.methods_conditional.align_ref_type.align_mass_column]

            align_peak_reference = align_reference_column[align_reference_column>=min(mz(msidata)) & align_reference_column<=max(mz(msidata))]
            if (length(align_peak_reference) == 0)
            {align_peak_reference = 0
            }



#### if length <0: align_peak_reference = 0

        
        #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_msidata_ref':
                  loadRData <- function(fileName){
                  #loads an RData file, and returns it
                  load(fileName)
                  get(ls()[ls() != "fileName"])
                }
                align_peak_reference = loadRData('$method.methods_conditional.align_ref_type.align_peaks_msidata')
        #end if

        #if str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'diff':
            print('diff peakalignment')
            msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',diff.max =$method.methods_conditional.methods_for_alignment.value_diffalignment, units = "$method.methods_conditional.methods_for_alignment.units_diffalignment", ref=align_peak_reference)

       #elif str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'DP':
            print('DPpeakalignment')
        msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',gap = $method.methods_conditional.methods_for_alignment.gap_DPalignment, ref=align_peak_reference)
       #end if

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))
            medint = round(median(spectra(msidata)[]), digits=2)
            TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
            aligned= c(maxfeatures, medianpeaks, medint, TICs)
            QC_numbers= cbind(QC_numbers, aligned)
            ### preparation for QC plots
            vectorofactions = append(vectorofactions, "aligned")

            for (calibrant in inputcalibrants)
            {
                currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                               par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="aligned")
              
               assign(paste("aligned",calibrant, sep="_"), currentimage)
            }
        #end if

    #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering':
        print('Peak_filtering')
        msidata = peakFilter(msidata, method='freq', freq.min = $method.methods_conditional.frequ_filtering)

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))
            medint = round(median(spectra(msidata)[]), digits=2)
            TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
            filtered= c(maxfeatures, medianpeaks, medint, TICs)
            QC_numbers= cbind(QC_numbers, filtered)
            ### preparation for QC plots
            vectorofactions = append(vectorofactions, "filtered")

            for (calibrant in inputcalibrants)
            {
                currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                               par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="filtered")
              
               assign(paste("filtered",calibrant, sep="_"), currentimage)
            }
        #end if

    #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction':
        print('Data_reduction')

        #if str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'bin':
            print('bin reduction')
            msidata = reduceDimension(msidata, method="bin", width=$method.methods_conditional.methods_for_reduction.bin_width, units="$method.methods_conditional.methods_for_reduction.bin_units", fun=$method.methods_conditional.methods_for_reduction.bin_fun)

        #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'resample':
            print('resample reduction')
            msidata = reduceDimension(msidata, method="resample", step=$method.methods_conditional.methods_for_reduction.resample_step)

        #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'peaks':
            print('peaks reduction')

            #if str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'table':
                reference_table = read.delim("$method.methods_conditional.methods_for_reduction.ref_type.peaks_table", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)
                reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.mass_column]
                peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))]

            #elif str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref':
                      loadRData <- function(fileName){
                      #loads an RData file, and returns it
                      load(fileName)
                      get(ls()[ls() != "fileName"])
                    }
                    peak_reference = loadRData('$method.methods_conditional.methods_for_reduction.ref_type.peaks_msidata')
            #end if
            msidata = reduceDimension(msidata, method="peaks", ref=peak_reference, type="$method.methods_conditional.methods_for_reduction.peaks_type")
        #end if

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))
            medint = round(median(spectra(msidata)[]), digits=2)
            TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
            reduced= c(maxfeatures, medianpeaks, medint, TICs)
            QC_numbers= cbind(QC_numbers, reduced)
            ### preparation for QC plots
            vectorofactions = append(vectorofactions, "reduced")

            for (calibrant in inputcalibrants)
            {
                currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
                               par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 
                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="reduced")
              
               assign(paste("reduced",calibrant, sep="_"), currentimage)
            }
        #end if
    #end if
#end for

####################################################### Outputs: RData, tabular and QC report ######################################################


## save as (.RData)
save(msidata, file="$msidata_preprocessed")


#if $output_matrix:


    if (length(features(msidata))> 0)
    {

        ## save as intensity matrix

        spectramatrix = spectra(msidata)
        rownames(spectramatrix) = mz(msidata)
        newmatrix = rbind(pixels(msidata), spectramatrix)
        write.table(newmatrix[2:nrow(newmatrix),], file="$matrixasoutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")

    }else{
        print("file has no features left")
        write.table(matrix(rownames(coord(msidata)), ncol=ncol(msidata), nrow=1), file="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
    }

#end if


#if $outputs.outputs_select == "quality_control":

    rownames(QC_numbers) = c("# features", "median # peaks", "median intensity", "median TIC")
    grid.table(t(QC_numbers))

    for (calibrant in inputcalibrants)
    {
        imagelist = list()
      
        for (numberprepro in 1:length(vectorofactions)){
        
            imagelist[[numberprepro]] = get(paste(vectorofactions[numberprepro],calibrant, sep="_"))
        
        
        }
      
      do.call(grid.arrange,imagelist)
    }


    dev.off()

#end if


    ]]></configfile>
    </configfiles>
    <inputs>
        <param name="infile" type="data" format="imzml,rdata,analyze75"
            label="MSI rawdata as imzml, analyze7.5 or Cardinal MSImageSet saved as RData"
            help="load imzml and ibd file by uploading composite datatype imzml"/>
        <repeat name="methods" title="Preprocessing" min="1" max="50">
            <conditional name="methods_conditional">
                <param name="preprocessing_method" type="select" label="Select the preprocessing methods you want to apply">
                    <option value="Normalization" selected="True">Normalization to TIC</option>
                    <option value="Baseline_reduction">Baseline Reduction</option>
                    <option value="Smoothing">Peak smoothing</option>
                    <option value="Peak_picking">Peak picking</option>
                    <option value="Peak_alignment">Peak alignment</option>
                    <option value="Peak_filtering">Peak filtering</option>
                    <option value="Data_reduction">Data reduction</option>
                </param>

                <when value="Normalization"/>
                <when value="Baseline_reduction">
                    <param name="blocks_baseline" type="integer" value="50"
                        label="Blocks"/>
                </when>
                <when value="Smoothing">
                    <conditional name="methods_for_smoothing">
                        <param name="smoothing_method" type="select" label="Smoothing method">
                            <option value="gaussian" selected="True">gaussian</option>
                            <option value="sgolay">Savitsky-Golay</option>
                            <option value="ma">moving average</option>
                        </param>
                        <when value="gaussian">
                            <param name="sd_gaussian" type="float" value="4"
                                   label="The standard deviation for the Gaussian kernel (window/sd)"/>
                        </when>
                        <when value="sgolay">
                            <param name="order_of_filters" type="integer" value="3"
                                   label="The order of the smoothing filter"/>
                        </when>
                        <when value="ma">
                            <param name="coefficients_ma_filter" type="integer" value="1"
                                   label="The coefficients for the moving average filter"/>
                        </when>
                    </conditional>
                    <param name="window_smoothing" type="integer" value="9"
                                label="Window size"/>
                </when>
                <when value="Peak_picking">
                    <param name="SNR_picking_method" type="integer" value="3"
                        label="Signal to noise ratio"
                        help="The minimal signal to noise ratio for peaks to be considered as a valid peak."/>
                    <param name="blocks_picking" type="integer" value="100" label = "Number of blocks"
                        help="Number of blocks in which to divide mass spectrum to calculate noise"/>
                    <param name="window_picking" type="integer" value="5" label= "Window size" help="Window width for seeking local maxima"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" type="select" label="Peak picking method" help="only simple works for processed imzML files">
                            <option value="adaptive" selected="True">adaptive</option>
                            <option value="limpic">limpic</option>
                            <option value="simple">simple</option>
                        </param>
                        <when value="adaptive">
                            <param name="spar_picking" type="float" value="1.0"
                                label="Spar value" 
                                help = "Smoothing parameter for the spline smoothing 
                                  applied to the spectrum in order to decide the cutoffs 
                                  for throwing away false noise spikes that might occur inside peaks"/>
                        </when>
                        <when value="limpic">
                            <param name="tresh_picking" type="float" value="0.75"
                                label="thresh value" help="The thresholding quantile to use when comparing slopes in order to throw away peaks that are too flat"/>
                        </when> 
                        <when value="simple"/>
                    </conditional>
                </when>
                <when value="Peak_alignment">
                    <conditional name="methods_for_alignment">
                        <param name="alignment_method" type="select" label="Alignment method">
                            <option value="diff" selected="True">diff</option>
                            <option value="DP">DP</option>
                        </param>
                        <when value="diff">
                            <param name="value_diffalignment" type="integer" value="200"
                                   label="diff.max" help="Peaks that differ less than this value will be aligned together"/>
                            <param name="units_diffalignment" type="select" display = "radio" optional = "False"
                                   label="units" help="The coefficients for the moving average filter">
                                    <option value="ppm" selected="True">ppm</option>
                                    <option value="Da">Da</option>
                            </param>
                        </when>
                        <when value="DP">
                            <param name="gap_DPalignment" type="integer" value="0"
                                   label="Gap" help="The gap penalty for the dynamic programming sequence alignment"/>
                        </when>
                    </conditional>
                    <conditional name="align_ref_type">
                        <param name="align_reference_datatype" type="select" label="Choose reference">
                            <option value="align_noref" selected="True">no reference</option>
                            <option value="align_table" >tabular file as reference</option>
                            <option value="align_msidata_ref">msidata file as reference</option>
                        </param>
                        <when value="align_noref"/>
                        <when value="align_table">
                            <param name="align_peaks_table" type="data" format="tabular" 
                                label="Reference mz values to use for alignment - only these will be kept" help="One column with mz values (without empty cells or letters)"/>
                            <param name="align_mass_column" data_ref="align_peaks_table" label="Column with reference mz" type="data_column"/>
                        </when>
                        <when value="align_msidata_ref">
                            <param name="align_peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/>
                        </when>
                    </conditional>
                </when>
                <when value="Peak_filtering">
                    <param name="frequ_filtering" type="integer" value="1000"
                        label="Freq.min" help="Peaks that occur in the dataset fewer times than this will be removed. Number should be between 1 (no filtering) and number of spectra (pixel)"/>
                </when>
                <when value="Data_reduction">
                    <conditional name="methods_for_reduction">
                        <param name="reduction_method" type="select" label="Reduction method">
                            <option value="bin" selected="True">bin</option>
                            <option value="resample">resample</option>
                            <option value="peaks">peaks</option>
                        </param>
                        <when value="bin">
                            <param name="bin_width" type="float" value="1"
                                   label="The width of a bin in mz or ppm" help="Width must be greater than Range of mz values/Number of mz features"/>
                            <param name="bin_units" type="select" display="radio"
                                   label="Unit for bin">
                                    <option value="mz" selected="True">mz</option>
                                    <option value="ppm">ppm</option>
                            </param>
                            <param name="bin_fun" type="select" display="radio"
                                   label="Calculate sum or mean intensity for ions of the same bin">
                                    <option value="mean" selected="True">mean</option>
                                    <option value="sum">sum</option>
                            </param>
                        </when>
                        <when value="resample">
                            <param name="resample_step" type="float" value="1"
                                   label="The step size in mz" help="Step size must be greater than Range of mz values/Number of mz features"/>
                        </when>
                        <when value="peaks">
                            <param name="peaks_type" type="select" display="radio"
                                   label="Should the peak height or area under the curve be taken as the intensity value?">
                                    <option value="height" selected="True">height</option>
                                    <option value="area">area</option>
                            </param>                            
                            <conditional name="ref_type">
                                <param name="reference_datatype" type="select" label="Choose reference datatype">
                                    <option value="table" selected="True">tabular file</option>
                                    <option value="msidata_ref">msidata file</option>
                                </param>
                                <when value="table">
                                    <param name="peaks_table" type="data" format="tabular" 
                                        label="Reference mz values to use to reduce the dimension" help="One column with mz values (without empty cells or letters, mz outside mz range are not used for filtering)"/>
                                    <param name="mass_column" data_ref="peaks_table" label="Column with reference mz" type="data_column"/>
                                </when>
                                <when value="msidata_ref">
                                    <param name="peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/>
                                </when>
                            </conditional>
                        </when>
                    </conditional>
                </when>
            </conditional>
        </repeat>
        <conditional name="outputs">
            <param name="outputs_select" type="select" label="Quality control output">
                <option value="quality_control" selected="True">yes</option>
                <option value="no_quality_control">no</option>
            </param>
            <when value="quality_control">
                <param name="calibrant_file" type="data" format="tabular"
                 label="Provide a list of masses which will be plotted in the quality control report"
                 help="Use internal calibrant masses"/>
                 <param name="calibrants_column" data_ref="calibrant_file" label="Column with masses" type="data_column"/>
                 <param name="plusminus_dalton" value="0.25" type="text" label="Mass range" help="Plusminus mass window in Dalton"/>
            </when>
            <when value="no_quality_control"/>
        </conditional>
        <param name="output_matrix" type="boolean" display="radio" label="Intensity matrix output"/>
    </inputs>
    <outputs>
        <data format="rdata" name="msidata_preprocessed" label="Preprocessed ${on_string}"/>
        <data format="pdf" name="QC_plots" from_work_dir="Preprocessing.pdf" label = "QC preprocessing report on ${on_string}">
            <filter>outputs["outputs_select"] == "quality_control"</filter>
        </data>
        <data format="tabular" name="matrixasoutput" label="Intensity matrix ${on_string}">
            <filter>output_matrix</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>
                <composite_data value="Example_Continuous.ibd"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Smoothing" />
                    <conditional name="methods_for_smoothing">
                        <param name="smoothing_method" value="gaussian" />
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking" />
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="adaptive" />
                    </conditional>
                    <param name="blocks_picking" value="3" />
                    <param name="window_picking" value="3" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment" />
                    <conditional name="methods_for_alignment">
                        <param name="alignment_method" value="diff" />
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_filtering" />
                    <param name="frequ_filtering" value="2"/>
                </conditional>
            </repeat>
            <param name="outputs_select" value="no_quality_control"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results1.txt" />
        </test>
        <test>
            <param name="infile" value="example_continous.RData" ftype="rdata"/>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking" />
                    <param name="blocks_picking" value="3" />
                    <param name="window_picking" value="5" />
                    <param name="methods_for_picking" value="simple" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment" />
                    <param name="methods_for_alignment" value="DP" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results2.txt" lines_diff="2" />
            <output name="QC_plots" file="preprocessing_results2.pdf" compare="sim_size"/>
        </test>
        <test>
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking" />
                    <param name="blocks_picking" value="3" />
                    <param name="window_picking" value="5" />
                    <param name="methods_for_picking" value="limpic" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment" />
                    <param name="methods_for_alignment" value="diff" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size" />
            <output name="QC_plots" file="preprocessing_results3.pdf" compare="sim_size"/>
        </test>
        <test>
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Data_reduction" />
                    <param name="bin_width" value="0.1" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="no_quality_control"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results4.txt" />
        </test>
        <test>
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>
                <composite_data value="Example_Continuous.ibd"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Data_reduction" />
                    <param name="step_width" value="0.1" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results5.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results5.txt" />
            <output name="QC_plots" file="preprocessing_results5.pdf" compare="sim_size"/>
        </test>
    </tests>
    <help>
        <![CDATA[
Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets.

**Preprocessing steps:**

- Smoothening: Smoothing of the mass peaks reduces noise and improves peak detection
- Normalization: Normalization of intensities to total ion current (TIC)
- Baseline reduction: Baseline  reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets)
- Peak picking: relevant peaks are picked while noise-peaks are removed
- Peak alignment: after peak picking, mz inaccuracies are removed by alignment of same peaks to a common mz value
- Peak filtering: after peak picking and alignment, removes peaks that occur infrequently, such as those which only occur in a small proportion of pixels. If not sure which cutoff to chose run qualitycontrol first and decide according to the zero value plot.
- Data reduction: binning, resampling or peak filtering to reduce data
        ]]>
    </help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btv146</citation>
    </citations>
</tool>